






from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
							import tensorflow as tf

							from transformers import (
							    TFDebertaVaForMaskedLM,
							    TFDebertaVaForQuestionAnswering,
							    TFDebertaVaForSequenceClassification,
							    TFDebertaVaForTokenClassification,
							    TFDebertaVaModel,
							)

class 				lowerCAmelCase_	:


				def __init__(					self ,						_lowerCAmelCase ,						_lowerCAmelCase=1_3 ,						_lowerCAmelCase=7 ,						_lowerCAmelCase=True ,						_lowerCAmelCase=True ,						_lowerCAmelCase=True ,						_lowerCAmelCase=True ,						_lowerCAmelCase=9_9 ,						_lowerCAmelCase=3_2 ,						_lowerCAmelCase=2 ,						_lowerCAmelCase=4 ,						_lowerCAmelCase=3_7 ,						_lowerCAmelCase="gelu" ,						_lowerCAmelCase=0.1 ,						_lowerCAmelCase=0.1 ,						_lowerCAmelCase=5_1_2 ,						_lowerCAmelCase=1_6 ,						_lowerCAmelCase=2 ,						_lowerCAmelCase=0.02 ,						_lowerCAmelCase=False ,						_lowerCAmelCase=True ,						_lowerCAmelCase="None" ,						_lowerCAmelCase=3 ,						_lowerCAmelCase=4 ,						_lowerCAmelCase=None ,						):
											_lowercase							:   Any 							=   parent
											_lowercase							:   List[Any] 							=   batch_size
											_lowercase							:   List[Any] 							=   seq_length
											_lowercase							:   Union[str, Any] 							=   is_training
											_lowercase							:   Tuple 							=   use_input_mask
											_lowercase							:   Dict 							=   use_token_type_ids
											_lowercase							:   Tuple 							=   use_labels
											_lowercase							:   Union[str, Any] 							=   vocab_size
											_lowercase							:   str 							=   hidden_size
											_lowercase							:   Dict 							=   num_hidden_layers
											_lowercase							:   Optional[int] 							=   num_attention_heads
											_lowercase							:   int 							=   intermediate_size
											_lowercase							:   int 							=   hidden_act
											_lowercase							:   Optional[int] 							=   hidden_dropout_prob
											_lowercase							:   Optional[int] 							=   attention_probs_dropout_prob
											_lowercase							:   str 							=   max_position_embeddings
											_lowercase							:   Optional[int] 							=   type_vocab_size
											_lowercase							:   Dict 							=   type_sequence_label_size
											_lowercase							:   Any 							=   initializer_range
											_lowercase							:   Any 							=   num_labels
											_lowercase							:   Any 							=   num_choices
											_lowercase							:   List[str] 							=   relative_attention
											_lowercase							:   List[str] 							=   position_biased_input
											_lowercase							:   Any 							=   pos_att_type
											_lowercase							:   Any 							=   scope


				def 						__a					(					self      ):
											_lowercase							:   List[str] 							=   ids_tensor([self.batch_size, self.seq_length] ,						self.vocab_size      )

											_lowercase							:   int 							=   None
											if self.use_input_mask:
																		_lowercase							:   Union[str, Any] 							=   random_attention_mask([self.batch_size, self.seq_length]      )

											_lowercase							:   Union[str, Any] 							=   None
											if self.use_token_type_ids:
																		_lowercase							:   Dict 							=   ids_tensor([self.batch_size, self.seq_length] ,						self.type_vocab_size      )

											_lowercase							:   Dict 							=   None
											_lowercase							:   Optional[Any] 							=   None
											_lowercase							:   List[Any] 							=   None
											if self.use_labels:
																		_lowercase							:   Union[str, Any] 							=   ids_tensor([self.batch_size] ,						self.type_sequence_label_size      )
																		_lowercase							:   str 							=   ids_tensor([self.batch_size, self.seq_length] ,						self.num_labels      )

											_lowercase							:   Any 							=   DebertaVaConfig(
											    vocab_size=self.vocab_size ,						hidden_size=self.hidden_size ,						num_hidden_layers=self.num_hidden_layers ,						num_attention_heads=self.num_attention_heads ,						intermediate_size=self.intermediate_size ,						hidden_act=self.hidden_act ,						hidden_dropout_prob=self.hidden_dropout_prob ,						attention_probs_dropout_prob=self.attention_probs_dropout_prob ,						max_position_embeddings=self.max_position_embeddings ,						type_vocab_size=self.type_vocab_size ,						relative_attention=self.relative_attention ,						position_biased_input=self.position_biased_input ,						initializer_range=self.initializer_range ,						return_dict=__SCREAMING_SNAKE_CASE ,						)

											return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


				def 						__a					(					self ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase      ):
											_lowercase							:   List[Any] 							=   TFDebertaVaModel(config=__SCREAMING_SNAKE_CASE      )
											_lowercase							:   Any 							=   {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

											_lowercase							:   int 							=   [input_ids, input_mask]
											_lowercase							:   Union[str, Any] 							=   model(__SCREAMING_SNAKE_CASE      )

											_lowercase							:   Union[str, Any] 							=   model(__SCREAMING_SNAKE_CASE      )

											self.parent.assertEqual(result.last_hidden_state.shape ,						(self.batch_size, self.seq_length, self.hidden_size)      )


				def 						__a					(					self ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase      ):
											_lowercase							:   int 							=   TFDebertaVaForMaskedLM(config=__SCREAMING_SNAKE_CASE      )
											_lowercase							:   Any 							=   {
											    'input_ids': input_ids,
											    'attention_mask': input_mask,
											    'token_type_ids': token_type_ids,
											}
											_lowercase							:   int 							=   model(__SCREAMING_SNAKE_CASE      )
											self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.seq_length, self.vocab_size)      )


				def 						__a					(					self ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase      ):
											_lowercase							:   List[str] 							=   self.num_labels
											_lowercase							:   str 							=   TFDebertaVaForSequenceClassification(config=__SCREAMING_SNAKE_CASE      )
											_lowercase							:   Any 							=   {
											    'input_ids': input_ids,
											    'attention_mask': input_mask,
											    'token_type_ids': token_type_ids,
											}

											_lowercase							:   Dict 							=   model(__SCREAMING_SNAKE_CASE      )
											self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.num_labels)      )


				def 						__a					(					self ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase      ):
											_lowercase							:   Union[str, Any] 							=   self.num_labels
											_lowercase							:   Optional[Any] 							=   TFDebertaVaForTokenClassification(config=__SCREAMING_SNAKE_CASE      )
											_lowercase							:   Tuple 							=   {
											    'input_ids': input_ids,
											    'attention_mask': input_mask,
											    'token_type_ids': token_type_ids,
											}
											_lowercase							:   List[Any] 							=   model(__SCREAMING_SNAKE_CASE      )
											self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.seq_length, self.num_labels)      )


				def 						__a					(					self ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase      ):
											_lowercase							:   str 							=   TFDebertaVaForQuestionAnswering(config=__SCREAMING_SNAKE_CASE      )
											_lowercase							:   List[Any] 							=   {
											    'input_ids': input_ids,
											    'attention_mask': input_mask,
											    'token_type_ids': token_type_ids,
											}

											_lowercase							:   List[Any] 							=   model(__SCREAMING_SNAKE_CASE      )
											self.parent.assertEqual(result.start_logits.shape ,						(self.batch_size, self.seq_length)      )
											self.parent.assertEqual(result.end_logits.shape ,						(self.batch_size, self.seq_length)      )


				def 						__a					(					self      ):
											_lowercase							:   Tuple 							=   self.prepare_config_and_inputs()
											(
											    (
											    _lowercase      
											)							,							(
											    _lowercase      
											)							,							(
											    _lowercase      
											)							,							(
											    _lowercase      
											)							,							(
											    _lowercase      
											)							,							(
											    _lowercase      
											)							,							(
											    _lowercase      
											)							,							      
											)							:   Tuple 							=   config_and_inputs
											_lowercase							:   Any 							=   {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
											return config, inputs_dict




@require_tf
class 				lowerCAmelCase_	(		__snake_case   ,		__snake_case   ,		unittest.TestCase			):
				_UpperCamelCase					:       Dict							       = (
				    (
				        TFDebertaVaModel,
				        TFDebertaVaForMaskedLM,
				        TFDebertaVaForQuestionAnswering,
				        TFDebertaVaForSequenceClassification,
				        TFDebertaVaForTokenClassification,
				    )
				    if is_tf_available()
				    else ()
				)
				_UpperCamelCase					:       Tuple							       = (
				    {
				        """feature-extraction""": TFDebertaVaModel,
				        """fill-mask""": TFDebertaVaForMaskedLM,
				        """question-answering""": TFDebertaVaForQuestionAnswering,
				        """text-classification""": TFDebertaVaForSequenceClassification,
				        """token-classification""": TFDebertaVaForTokenClassification,
				        """zero-shot""": TFDebertaVaForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)

				_UpperCamelCase					:       Optional[int]							       = False
				_UpperCamelCase					:       Optional[Any]							       = False


				def 						__a					(					self      ):
											_lowercase							:   Dict 							=   TFDebertaVaModelTester(self      )
											_lowercase							:   int 							=   ConfigTester(self ,						config_class=__SCREAMING_SNAKE_CASE ,						hidden_size=3_7      )


				def 						__a					(					self      ):
											self.config_tester.run_common_tests()


				def 						__a					(					self      ):
											_lowercase							:   int 							=   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE      )


				def 						__a					(					self      ):
											_lowercase							:   int 							=   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_lm(*__SCREAMING_SNAKE_CASE      )


				def 						__a					(					self      ):
											_lowercase							:   Union[str, Any] 							=   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_question_answering(*__SCREAMING_SNAKE_CASE      )


				def 						__a					(					self      ):
											_lowercase							:   Tuple 							=   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_sequence_classification(*__SCREAMING_SNAKE_CASE      )


				def 						__a					(					self      ):
											_lowercase							:   List[Any] 							=   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_token_classification(*__SCREAMING_SNAKE_CASE      )


				@slow
				def 						__a					(					self      ):
											_lowercase							:   List[Any] 							=   TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge'      )
											self.assertIsNotNone(__SCREAMING_SNAKE_CASE      )




@require_tf
class 				lowerCAmelCase_	(		unittest.TestCase			):


				@unittest.skip(reason='Model not available yet'      )
				def 						__a					(					self      ):
											pass


				@slow
				def 						__a					(					self      ):
											_lowercase							:   Union[str, Any] 							=   TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge'      )
											_lowercase							:   Optional[Any] 							=   tf.constant([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]      )
											_lowercase							:   int 							=   tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]      )
											_lowercase							:   Dict 							=   model(__SCREAMING_SNAKE_CASE ,						attention_mask=__SCREAMING_SNAKE_CASE      )[0]

											_lowercase							:   List[str] 							=   tf.constant(
											    [[[0.23_56, 0.19_48, 0.03_69], [-0.10_63, 0.35_86, -0.51_52], [-0.63_99, -0.02_59, -0.25_25]]]      )
											tf.debugging.assert_near(output[:, 1:4, 1:4] ,						__SCREAMING_SNAKE_CASE ,						atol=1E-4      )












"""simple docstring"""





_SCREAMING_SNAKE_CASE   :							Optional[int]          =		8.314_4598







def 	lowerCamelCase__			(				_lowerCamelCase			:						float						,							_lowerCamelCase			:						float					) ->							float:
  if temperature < 0:
    raise Exception('Temperature cannot be less than 0 K'					)
  if molar_mass <= 0:
    raise Exception('Molar mass cannot be less than or equal to 0 kg/mol'					)
  else:
    return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_mass) ** 0.5


if __name__ == "__main__":
  import doctest

  # run doctest
  doctest.testmod()

  # example
  _SCREAMING_SNAKE_CASE   :							List[Any]          =		300
  _SCREAMING_SNAKE_CASE   :							Dict          =		28
  _SCREAMING_SNAKE_CASE   :							List[str]          =		rms_speed_of_molecule(temperature, molar_mass)
  print(F'''Vrms of Nitrogen gas at 300 K is {vrms} m/s''')




